Kulkarni / Shivananda / Krishnan Applied Recommender Systems with Python

Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
1. Auflage 2022
ISBN: 978-1-4842-8954-9
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 248 Seiten, eBook

Reihe: Apress Access Books

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.

You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.

By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.

What You Will Learn

• Understand and implement different recommender systems techniques with Python
• Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization 
• Build hybrid recommender systems that incorporate both content-based and collaborative filtering
• Leverage machine learning, NLP, and deep learning for building recommender systems

Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Kulkarni, Akshay

Shivananda, Adarsha

Kulkarni, Anoosh

Krishnan, V Adithya

Inhaltsverzeichnis

Chapter 1: Introduction to Recommender SystemsChapter Goal: Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of pages: 25Sub - Topics:  
1. Intro to recommender system 2. How it works3. Types and how they worka. Association rule miningb. Content basedc. Collaborative filtering d. Hybrid systemse. ML Clustering basedf. ML Classification basedg. Deep learning and NLP basedh. Graph based
Chapter 2: Association Rule MiningChapter Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics 1 APRIORI2 FP GROWTH3 Advantages and Disadvantages
Chapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics 1 TF-IDF2 BOW3 Transformer based4 Advantages and disadvantages

Chapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 KNN – item based2 KNN – user based3 Advantages and disadvantages

Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 Latent factors2 SVD3 ALS4 Advantages and disadvantages

Chapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics: 1 Weighted: a different weight given to the recommenders of each technique used to favor some of them.2 Mixed: a single set of recommenders, without favorites.3 Augmented: suggestions from one system are used as input for the next, and so on until the last one.4 Switching: Choosing a random method5 Advantages and disadvantages

Chapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics: 1 K means clustering2 Hierarchal clustering 3 Advantages and disadvantages

Chapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics: 1 Buying propensity model2 Logistic regression3 Random forest4 SVM5 Advantages and disadvantages

Chapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics: 1 Word embedding’s2 Deep neural networks3 Advantages and disadvantages

Chapter 10: Graph-Based Recommender SystemChapter Goal: Implementing graph-based recommender system using Python for computation performanceNo of pages: 25Sub - Topics: 1 Generating nodes and edges2 Building algorithm3 Advantages and disadvantages

Chapter 11: Emerging Areas and Techniques in Recommender System Chapter Goal: To get an overview of the new and emerging techniques and the areas of research in Recommender systemsNo of pages: 15Sub - Topics: 1 Personalized recommendation engine2 Context-based search engine3 Multi-objective recommendations4 Summary

Über Autor(innen)

Akshay R Kulkarni is an AI and machine learning evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, Author, and a regular speaker at major AI and data science conferences including Strata, O’Reilly AI Conf, and GIDS. He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is Data science and MLOps Leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Anoosh Kulkarni is a data scientist and an AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.

V Adithya Krishnan is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is developing a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, “Deep Learning Based Approach for Range Estimation”, written in collaboration with the DRDO. He lives in Chennai with his family.